我试图使用scipy.optimize函数curve_fit使用自定义指数函数拟合一组数据点 . 我的代码如下:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def fit(x, tau, beta):
return np.exp(-1 * np.power(x / tau, beta))
def plot_e2e(times, e2es):
optimalParams, covariance = curve_fit(fit, times, e2es)
tau = optimalParams[0]
beta = optimalParams[1]
print 'Tau is:', tau
print 'Beta is:', beta
if __name__ == '__main__':
% read_e2e_data not included for proprietary reasons.
times, e2es = read_e2e_data(fileName)
plot_e2e(times, e2es)
这样做会引发以下异常(由于取出不相关的内容,行号可能会有所不同):
Traceback (most recent call last):
File ".\plot_e2e.py", line 54, in <module>
plot_e2e(times, e2es)
File ".\plot_e2e.py", line 34, in plot_e2e
optimalParams, covariance = curve_fit(fit, times, e2es)
File "C:\Anaconda\lib\site-packages\scipy\optimize\minpack.py", line 586, in curve_fit
raise RuntimeError(msg)
RuntimeError: Optimal parameters not found: Number of calls to function has reached maxfev = 600.
如果我增加curve_fit的maxfev参数,则改为获取Tau(4.035e-303)的伪值 .
因此,我的时间和e2e向量是:
time = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 11.0, 14.0, 17.0, 21.0, 25.0, 30.0, 37.0, 45.0, 54.0, 65.0, 78.0, 94.0, 113.0, 136.0, 163.0, 196.0, 236.0, 283.0, 340.0, 409.0, 491.0]
e2es = [1.0, 0.999804, 0.99964, 0.999497, 0.99937, 0.999276, 0.999139, 0.998974, 0.998566, 0.998005, 0.997225, 0.997073, 0.997793, 0.998586, 1.001542, 1.004414, 1.005311, 1.001431, 1.001016, 0.998936, 0.995649, 0.993765, 0.98663, 0.985266, 0.984635, 0.982588, 0.974413, 0.973811, 0.968772, 0.970131]
如果您对可能存在的问题有任何想法,请告诉我 . 我一直在尝试调试这个问题并且已经走到了尽头 .
1 回答
你得到的错误基本上意味着Scipy寻找正确的契合并没有设法收敛 . Scipy定义了一个名为maxfev的值,其目的是在它放弃搜索的迭代次数之后 . 您可以更改此参数:
希望这可以帮助!